Learning-Based Camera Pose Estimation From Images of an Environment

ABSTRACT

A deep neural network (DNN) system learns a map representation for estimating a camera position and orientation (pose). The DNN is trained to learn a map representation corresponding to the environment, defining positions and attributes of structures, trees, walls, vehicles, walls, etc. The DNN system learns a map representation that is versatile and performs well for many different environments (indoor, outdoor, natural, synthetic, etc.). The DNN system receives images of an environment captured by a camera (observations) and outputs an estimated camera pose within the environment. The estimated camera pose is used to perform camera localization, i.e., recover the three-dimensional (3D) position and orientation of a moving camera, which is a fundamental task in computer vision with a wide variety of applications in robot navigation, car localization for autonomous driving, device localization for mobile navigation, and augmented/virtual reality.

CLAIM OF PRIORITY

This application claims the benefit of U.S. Provisional Application No.62/569,299 (Attorney Docket No. NVIDP1189+/17SC0166US01) titled“Learning an Optimal Map Representation for Camera Tracking,” filed Oct.6, 2017, the entire contents of which is incorporated herein byreference.

FIELD OF THE INVENTION

The present invention relates to determining a camera pose, and inparticular, to determining the camera pose using a neural network.

BACKGROUND

Camera localization, i.e., recovering the three-dimensional (3D)position and orientation of a moving camera is one of the fundamentaltasks in computer vision with a wide variety of applications in robotnavigation, car localization for autonomous driving, device localizationfor mobile navigation, and augmented/virtual reality. A key component incamera localization is the concept of a map. A map is an abstractsummary of the input data that establishes geometric constraints betweenobservations and can be used to establish correspondences betweenconsecutive image frames. A map can be queried to obtain the camera posefor correcting drift in relative pose estimation and reinitialize thecamera pose if the tracking is lost. Maps, however, are usually definedin an application-specific manner with hand-crafted features. Examplesinclude 3D landmarks, 3D points, line/edge structures forindoor/man-made scenes, groups of pixels with depth, or object-levelcontext for semantic techniques. Being application-specific, the maprepresentations may ignore useful (sometimes, the only available)features in environments and thus may not be optimal or robust forgeneral scenarios. There is a need for addressing these issues and/orother issues associated with the prior art.

SUMMARY

A deep neural network (DNN) system learns a map representation forcamera localization. The DNN system receives images of an environmentcaptured by a camera (observations) and outputs a camera pose (positionand orientation) within the environment. The DNN is trained to determinea map representation corresponding to the environment, definingpositions and attributes of structures, trees, walls, vehicles, walls,etc. Examples of conventional map representation types includethree-dimensional (3D) points, line/edge structures for indoor/man-madescenes, and groups of pixels with depth. Typically, one type of maprepresentation is used that is best-suited for the particularenvironment. In contrast, the DNN system learns a map representationthat is versatile and performs well for many different environments(indoor, outdoor, natural, synthetic, etc.). The map representation maybe learned by the DNN and updated using supervised training and/orself-supervised training. Additionally, the DNN may be trained by fusingadditional multi-sensory inputs with the camera poses estimated by theDNN to improve accuracy of the DNN.

A method, computer readable medium, and system are disclosed forestimating a camera pose. Weights of a DNN are determined duringtraining using a labeled training dataset including images andcorresponding absolute camera poses and relative camera poses, where theweights define a map representation of an environment. An input image isreceived at the DNN. The DNN applies the weights to the input image togenerate an estimated camera pose for capturing the environment toproduce the input image.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A illustrates a block diagram of a camera pose estimation system,in accordance with an embodiment.

FIG. 1B illustrates a conceptual diagram of the camera poses and inputsto the camera pose estimation system, in accordance with an embodiment.

FIG. 1C illustrates a flowchart of a method for estimating camera pose,in accordance with an embodiment.

FIG. 1D illustrates a block diagram of another camera pose estimationsystem, in accordance with an embodiment.

FIG. 1E illustrates a flowchart of another method for estimating camerapose, in accordance with an embodiment.

FIG. 2A illustrates a block diagram of another camera pose estimationsystem, in accordance with an embodiment.

FIG. 2B illustrates a flowchart of a method for estimating camera poseusing the camera pose estimation system shown in FIG. 2A, in accordancewith an embodiment.

FIG. 2C illustrates camera localization results, in accordance with anembodiment.

FIG. 3 illustrates a parallel processing unit, in accordance with anembodiment.

FIG. 4A illustrates a general processing cluster within the parallelprocessing unit of FIG. 3, in accordance with an embodiment.

FIG. 4B illustrates a memory partition unit of the parallel processingunit of FIG. 3, in accordance with an embodiment.

FIG. 5A illustrates the streaming multi-processor of FIG. 4A, inaccordance with an embodiment.

FIG. 5B is a conceptual diagram of a processing system implemented usingthe PPU of FIG. 3, in accordance with an embodiment.

FIG. 5C illustrates an exemplary system in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented.

DETAILED DESCRIPTION

During inferencing, a DNN system receives a sequence of images andgenerates a sequence of camera poses using weights. The DNN systemlearns a map representation of an environment for camera localization.In the context of the following description, camera localizationcomprises determining a camera pose that includes a position andorientation. The map representation corresponds to the environment,defining positions and attributes of structures, trees, walls, vehicles,walls, etc. Examples of conventional map representation types includethree-dimensional (3D) points, line/edge structures for indoor/man-madescenes, and groups of pixels with depth. Typically, one type of maprepresentation is used that is best-suited for the particularenvironment. In contrast with conventional camera localization systems,the DNN system learns a map representation that is versatile andperforms well for many different environments (indoor, outdoor, natural,synthetic, etc.). The DNN system receives images of an environmentcaptured by a camera (observations) and outputs a camera pose within theenvironment. The DNN system is initially trained using labelled trainingdata.

FIG. 1A illustrates a block diagram of a camera pose estimation system100, in accordance with an embodiment. Although the camera poseestimation system 100 is described in the context of a DNN model, thecamera pose estimation system 100 may also be implemented by a program,custom circuitry, or by a combination of custom circuitry and a program.For example, the camera pose estimation system 100 may be implementedusing a GPU (graphics processing unit), CPU (central processing unit),or any processor capable of performing the operations described herein.Furthermore, persons of ordinary skill in the art will understand thatany system that performs the operations of the camera pose estimationsystem 100 is within the scope and spirit of embodiments of the presentinvention.

As shown in FIG. 1A, the camera pose estimation system 100 includes apair of DNNs 110, a training loss unit 105, and a relative posecomputation unit 120. In an embodiment, the DNN 110 is a deepconvolutional neural network (CNN). In an embodiment, the DNN 110comprises at least a convolutional neural network layer, followed by aglobal average pooling layer, followed by a fully-connected layer tooutput the estimated camera pose. The camera pose estimation system 100is trained to learn a map representation for a particular environment.Importantly, the camera pose estimation system 100 is versatile and maybe trained to learn different types of map representations. Duringsupervised and/or self-supervised training, parameters (weights CO ofthe DNNs 110 are updated.

Each DNN 110 receives an image (e.g., pixel values) and generates anestimated camera pose based on the weights. The DNNs 110 are trained tolearn the map representation that is defined by the weights and used bythe DNNs 110 to process an input image pair of the images I_(i) andI_(j) and produce corresponding estimated camera pose pairs p_(i) andp_(j). The input images may by captured from two different poses in theenvironment. For example, the input images may correspond to a paththrough the environment and be one after the other in sequence. Inanother example, the input images may be from two different points alongthe path that are separated by any distance. In other words, zero, one,two, or any number of intervening images may occur between the inputimages I_(i) and I_(j) along the path through the environment.

The relative pose computation unit 120 computes a relative estimatedcamera pose v_(ij) using the estimated camera pose pair. The trainingloss unit 105 receives the estimated pose pair, relative estimatedcamera pose, a ground truth (absolute) camera pose p* corresponding toone of the input images, and a ground truth relative pose v*corresponding to the input images. In an embodiment, the ground truthrelative pose v* is computed from the input images included in atraining dataset that includes at least the input images, where eachinput image is paired with a ground truth camera pose. The training lossunit 105 computes a loss function and updates the weights used by theDNNs 110. In an embodiment, the weights are modified to simultaneouslyreduce differences between the relative estimated camera poses and theground truth relative camera poses and differences between the estimatedcamera poses generated by the DNN 110 and the ground truth camera poses.

More illustrative information will now be set forth regarding variousoptional architectures and features with which the foregoing frameworkmay be implemented, per the desires of the user. It should be stronglynoted that the following information is set forth for illustrativepurposes and should not be construed as limiting in any manner. Any ofthe following features may be optionally incorporated with or withoutthe exclusion of other features described.

FIG. 1B illustrates a conceptual diagram of the camera poses and inputsto the camera pose estimation system, in accordance with an embodiment.Ground truth camera poses p₁, p₂, and p₃ are each associated with aground truth input image (I₁, I₂, and I₃). The ground truth input imagecorresponding to a ground truth camera pose is the image of theenvironment that is captured by a camera configured (in position andorientation) within the environment according to the ground truth camerapose. The ground truth camera poses p₁ and p₂ are each associated with aground truth relative camera pose v₁₂ and v₂₃.

The ground truth input images are input to the camera pose estimationsystem 100 to generate estimated camera poses and estimated relativecamera poses. The training loss unit 105 receives the estimated cameraposes, estimated relative camera poses, the ground truth camera poses,and the ground truth relative camera poses and generates updated weightsaccording to a loss function. When a desired accuracy level is achievedby the camera pose estimation system 100, supervised training of thecamera pose estimate system 100 is complete and the environment map isdefined by the weights. The accuracy level is indicated by differencesbetween the estimated camera poses and the ground truth camera poses anddifferences between the estimated relative camera poses and the groundtruth relative camera poses.

Importantly, the environment map is learned in a data-driven manner.Therefore, a variety of different environments, each having anassociated training dataset, may be learned by camera pose estimationsystem 100. Following supervised training, the camera pose estimatesystem 100 may be deployed to generate estimated camera poses based oninput images received in real time from a camera or images from anothersource. In an embodiment, after supervised training is completed, therelative pose computation unit 120 and the training loss unit 105 areidle and may be removed or reconfigured.

In an embodiment, the camera pose estimation system 100 includes only asingle DNN 110, and a single estimated camera pose is generated for eachinput image. In such an embodiment, the single estimated camera pose isstored within the camera pose estimate system 100 and used by therelative pose computation unit 120 and the training loss unit 105 toupdate the weights.

FIG. 1C illustrates a flowchart of a method 130 for estimating camerapose, in accordance with an embodiment. Although method 130 is describedin the context of the camera pose estimation system 100, the method 130may also be performed by a program, custom circuitry, or by acombination of custom circuitry and a program. For example, the method130 may be executed by a GPU (graphics processing unit), CPU (centralprocessing unit), or any processor capable of performing the camera poseestimation operations. Furthermore, persons of ordinary skill in the artwill understand that any system that performs method 130 is within thescope and spirit of embodiments of the present invention.

At step 135, the DNN 110 is trained by determining weights using alabeled training dataset including images and corresponding absolutecamera poses and relative camera poses, where weights of the DNN 110define a map representation of an environment. At step 140, an inputimage is received at the DNN 110. At step 145, the DNN 110 applies theweights to the input image to generate an estimated camera pose forcapturing the environment to produce the input image.

Conventional camera localization systems train a neural network usingsingle images labelled with absolute camera poses. In contrast, thecamera pose estimation system 100 is trained using geometric constraintsbetween pairs of observations that are included as an additional lossterm to update the weights. In an embodiment, the geometric constraintsare provided by the relative estimated camera pose v_(ij). Thus, thetraining is a geometry-aware learning technique that can significantlyimprove camera localization performance.

The DNN 110 is trained to estimate a camera pose from an input RGB imageIon the training set

={(I, p*)} via supervised learning, ƒ(I, Θ)=p, where camera pose p=(t,w), t is a position, e.g., (x,y,z) coordinates and w is an orientation(direction). The main difference between the training of DNN 110 forestimating camera poses and other training techniques, is that the lossof the per-image absolute pose and the loss of the relative pose betweenimage pairs are simultaneously minimized. In an embodiment, thefollowing loss function is used to simultaneously reduce differencesbetween the relative estimated camera poses and the ground truthrelative camera poses and differences between the estimated camera posesgenerated by the DNN 110 and the ground truth camera poses.

L

(Λ)=

h(p _(i) ,p _(i)*)+α

h(v _(ij) ,v _(ij)*),  (1)

where v_(ij)=(t_(i)−t_(j), w_(i)−w_(j)) is the relative camera posebetween pose predictions p_(i) and p_(j) for images I_(i) and I_(j).h(⋅) is a function to measure the distance between the predicted camerapose p and the ground truth camera pose p*, defined as:

h(p,p*)=∥t−t*∥ ₁ e ^(−β) +β+∥w−w*∥ ₁ e ^(−γ)+γ,  (2)

where β and γ are weights that balance a translation loss and a rotationloss. β and γ are initialized as β₀ and γ₀ and both are learned duringtraining. (I_(i), I_(j)) are image pairs within each tuple of s imagessampled with a gap of k frames from

. Intuitively, adding the loss contribution of the relative camera posesbetween image pairs helps to enforce global consistency, which improvesthe performance of camera localization. In an embodiment, the weightcoefficient α=1, β₀=0.0, and γ₀=−3.0. In an embodiment, image pairs aresampled from tuples of size s=3 with spacing k=10 frames.

In an embodiment, the camera orientation is parameterized as thelogarithm of a unit quaternion to represent rotation, and is bettersuited for regression of a DNN. Conventional techniques use 4-d unitquaternions to represent and regress the camera orientation with a l₁ orl₂ norm loss function. Issues with using 4-d unit quaternions are (1)the quadruple is an over parameterization of the 3 degree of freedom(DoF) rotation and (2) normalization of the output quadruple isrequired, but often results in worse performance. Conventionaltechniques may also use Euler angles which are not over-parameterized.However, Euler angles are not suited for regression since they wraparound 2π.

The logarithm of a unit quaternion, log q has 3 dimensions and is notover-parameterized. Therefore, the l₁ or l₂ distance is directly used asthe loss function without normalization. The logarithm of a unitquaternion q=(u, v), where u is a scalar and v is a 3-d vector, isdefined as:

$\begin{matrix}{{\log \mspace{14mu} q} = \left\{ \begin{matrix}{{\frac{v}{v}\cos^{- 1}u},} & {{{if}\mspace{14mu} {v}} \neq 0} \\{0,} & {otherwise}\end{matrix} \right.} & (3)\end{matrix}$

The logarithmic form w=log q can be converted back to a unit quaternionby the formula exp w=(cos∥w∥,

$\frac{w}{w}$

sin∥w∥). Using the logarithmic rotation parameterization achieves betterresults than conventional techniques, in terms of estimated camera poseaccuracy.

Conventional camera localization systems are offline techniquesrequiring supervised training, fixing values of parameters used by theneural network after training is completed. In contrast, the camera poseestimation system 100 may be modified to use geometric constraintsbetween pairs of observations to continuously update the weights (i.e.,defined map representation) used by the DNNs 110. While supervisedtraining may be performed using a labelled training dataset thatincludes ground truth camera poses and ground truth relative cameraposes, self-supervised training may be performed, without requiringground truth data, as additional unlabeled images are received. Theunlabeled images may be received during inferencing when the camera poseestimation system 100 is deployed. For example, the unlabeled images mayinclude videos captured at different times or camera motions in theenvironment.

FIG. 1D illustrates a block diagram of another camera pose estimationsystem 125, in accordance with an embodiment. Although the camera poseestimation system 125 is described in the context of a DNN model, thecamera pose estimation system 125 may also be implemented by a program,custom circuitry, or by a combination of custom circuitry and a program.For example, the camera pose estimation system 125 may be implementedusing a GPU (graphics processing unit), CPU (central processing unit),or any processor capable of performing the operations described herein.Furthermore, persons of ordinary skill in the art will understand thatany system that performs the operations of the camera pose estimationsystem 125 is within the scope and spirit of embodiments of the presentinvention.

As shown in FIG. 1D, the camera pose estimation system 125 includes thepair of DNNs 110, a training loss unit 115, and the relative posecomputation unit 120. The camera pose estimation system 125 learns a maprepresentation for a particular environment through supervised and/orself-supervised training. During supervised and/or self-supervisedtraining, parameters (weights Θ) of the DNNs 110 are updated. In anembodiment, the training loss unit 115 may be configured to perform theoperations of the training loss unit 105, enabling supervised learningand self-supervised learning. In another embodiment, the training lossunit 105 is also included within the camera pose estimation system 125.

In contrast with the camera pose estimation system 100, the trainingloss unit 115 receives geometric constraints in the form of sensor data.The geometric constraints can come from a variety of sources: poseconstraint from visual odometry (VO) between pairs of images,translation constraint from two global position sensor (GPS) readings(providing relative 3D position measurements), rotation constraint fromtwo inertial measurement unit (IMU) readings (providing relativerotation measurements), and the like. Furthermore, IMU and GPS providemeasurements about camera pose that are especially useful forchallenging conditions (e.g., textureless, low-light). As shown in FIG.1D, the inputs {circumflex over (p)}_(i) and {circumflex over (v)}_(ij)are GPS and visual odometry measurements, respectively. In the contextof the following description, the data received during simultaneousinferencing and self-supervised training (in addition to the inputimages) is additional data

.

The training loss unit 115 receives the estimated pose pair, relativeestimated camera pose v_(ij) from the relative pose computation unit120, and the additional data

including {circumflex over (p)}_(i) and {circumflex over (v)}_(ij). Thetraining loss unit 115 computes a loss function and updates the weightsused by the DNNs 110. In an embodiment, the weights are modified tosimultaneously reduce differences between the relative estimated cameraposes and {circumflex over (v)}_(ij) and differences between theestimated camera poses generated by the DNN 110 and {circumflex over(p)}_(i).

Suppose the additional data are some videos of the same scene,

={I_(t)}. Additional relative camera poses {circumflex over (v)}_(ij)may be computed between consecutive frames (I_(t) and I_(t+1)) in one ofthe videos using conventional visual odometry algorithms. In order toupdate the defined map representation with

, weights Θ of the camera pose estimation system 125 are fine-tuned byminimizing a loss function that consists of the original loss from thelabelled dataset

and the loss from the unlabeled data

,

L(Θ)=

(Θ)+

(Θ),  (4)

where

(Θ) is the distance between the relative camera pose v_(ij) (frompredictions p_(i), p_(j)) and the additional data, relative camera poses{circumflex over (v)}_(ij), and

(Θ)=

h(v _(ij) ,{circumflex over (v)} _(ij)).  (5)

Since visual odometry algorithms compute {circumflex over (v)}_(ij) inthe coordinate system of camera i, the relative estimated camera posev_(ij) is also computed in the coordinate system of camera i:

v _(ij)=(exp(w _(j))(t _(i) −t _(j))exp(w _(j))⁻¹,

log(exp(w _(j))⁻¹ exp(w _(i))).  (6)

Importantly, the supervision loss

(Θ) from the labelled dataset

is included in the loss function of equation (4) to avoid trivialsolutions resulting from optimizing only the self-supervised loss

(Θ) from

. In an embodiment, for supervised and self-supervised training,mini-batches of training data sample a portion from the labelled data

and the remainder from the unlabeled data

. The image pairs (I_(i), I_(j)) are sampled similarly from tuples of simages with a gap of k frames from both

and

. More specifically, suppose there are N images in an input sequence,I₁, . . . , I_(N). Within each tuple of s images, each two neighboringelements will form an image pair for training. For example, both(I_(i),I_(i+k)) and (I_(i+k (s-2)), I_(i+k (s-1))) are valid imagepairs.

The camera pose estimation system 125 exploits the complementarycharacteristics of visual odometry and DNN-based camera poseprediction—the visual odometry is locally accurate but often drifts overtime, and DNN-based camera pose predictions are noisy but drift-free.For other sensors such as IMU (which measures relative rotation) and GPS(which measures 3D locations), similar loss terms

(Θ) may be defined and computed by the training loss unit 115 tominimize the difference between such measurements and the estimates(predictions) generated by the DNN 110.

FIG. 1E illustrates a flowchart of another method 150 for estimatingcamera pose, in accordance with an embodiment. Although method 150 isdescribed in the context of the camera pose estimation system 125, themethod 150 may also be performed by a program, custom circuitry, or by acombination of custom circuitry and a program. For example, the method150 may be executed by a GPU (graphics processing unit), CPU (centralprocessing unit), or any processor capable of performing the camera poseestimation operations. Furthermore, persons of ordinary skill in the artwill understand that any system that performs method 150 is within thescope and spirit of embodiments of the present invention.

Step 135 is completed, as previously described, to train the DNN 110using a labeled training dataset including images and correspondingabsolute camera poses and relative camera poses. At step 155, an inputimage is received at the DNN 110. Unlike the input images receivedduring supervised training, the input image received at the DNN 110 isnot labelled and is therefore associated with a ground truth absolutecamera pose and a ground truth relative camera pose. At step 160, theDNN 110 applies the weights to the input image to generate an estimatedcamera pose for capturing the environment to produce the input image. Inan embodiment, a rotation portion of the estimated camera pose isparameterized as a 3D logarithm of a unit quaternion.

At step 165, a relative estimated camera pose is computed by therelative pose computation unit 120. At step 170, the training loss unit115 updates the weights based on a loss function computed using therelative estimated camera pose and additional data. In an embodiment,the additional data includes visual odometry data corresponding to theinput image and the weights of the DNN 110 are updated to minimizedifferences between a relative camera pose computed using the visualodometry data and the relative estimated camera pose. In an embodiment,the additional data includes GPS data corresponding to the input imageand the weights of the DNN 110 are updated to minimize differencesbetween the GPS data and the estimated camera pose. In an embodiment,the additional data includes inertial measurement data corresponding tothe input image and the weights of the DNN 110 are updated to minimizedifferences between the inertial measurement data and the estimatedcamera pose.

FIG. 2A illustrates a block diagram of another camera pose estimationsystem 200, in accordance with an embodiment, in accordance with anembodiment. The camera pose estimation system 200 includes the camerapose estimation system 100 or 125 or a combination of the camera poseestimation system 100 or 125. In additional, a pose graph optimizationunit 215 refines the estimated camera poses.

The purpose of pose graph optimization (PGO) is to refine the inputcamera poses such that the refined camera poses are close to the inputcamera poses (from the camera pose estimation system 100 or 125), andthe relative transforms between the refined camera poses agree with theinput relative camera poses {circumflex over (v)}_(ij). PGO is aniterative optimization process. During inferencing, the pose graphoptimization unit 215 fuses the estimated camera pose from the camerapose estimation system 100 or 125 and the relative camera poses{circumflex over (v)}_(ij) from visual odometry to produce smooth andglobally consistent camera pose predictions p_(i) ^(o) and p_(j) ^(o).Moreover, at runtime (i.e., during inferencing), the complementary noisecharacteristics of the estimated camera poses (locally noisy butdrift-free) and visual odometry (locally smooth but drifty) areexploited by fusing using a moving window technique with PGO.

A moving-window of T frames is used the initial poses predicted bycamera pose estimation system 100 or 125 are {p_(i)}_(i=1) ^(T), and therelative poses between two frames from visual odometry are {{circumflexover (v)}_(ij)} where i,j∈[1, T], i≠j. Combining the camera poseestimation system 100 or 125 with the pose graph optimization unit 215solves for the optimal camera poses {p_(i) ^(o)}_(i=1) ^(T) byminimizing the following loss function (cost):

L _(PGO)({p _(i) ^(o)}_(i=1) ^(T))=Σ_(i=1) ^(T) h (p _(i) ^(o) ,p_(i))+Σ_(i,j=1,i≠j) ^(T) h (v _(ij) ^(o) ,{circumflex over (v)}_(ij)),   (7)

where h(⋅) is a pose distance function shown as Equation (10) below. PGOis an iterative algorithm where internally v_(ij) ^(o) is derived fromp_(i) ^(o) and p_(j) ^(o) as in Equation (6). Note, the DNN 110 weightsΘ are fixed and only {p_(i) ^(o)}_(i=1) ^(T) is optimized. Combining thecamera pose estimation system 100 or 125 with the pose graphoptimization unit 215 further improves the accuracy of the estimatedcamera poses, with a minimal extra computational cost at testing.

The estimated camera poses {p_(i)}_(i=1) ^(T) and the relative cameraposes from visual odometry {{circumflex over (v)}_(ij)} are6-dimensional (3d translation t+3d log quaternion w). For the remainderof the PGO algorithm description, the log quaternions are converted tounit quaternion using the exponential map:

$\begin{matrix}{q = {\left( {{\cos {w}},{\frac{w}{w}\sin {w}}} \right).}} & (8)\end{matrix}$

A state vector z is the concatenation of all T camera pose vectors. Thetotal objective function is the sum of the costs of all constraints. Theconstraints can be either for the absolute pose or for the relative posebetween a pair of poses. For both of these categories, there areseparate constraints for translation and rotation:

$\begin{matrix}{{{E(z)} = {{\sum\limits_{c}{E_{c}(z)}} = {\sum_{c}{\overset{\_}{h}\left( {{f_{c}(c)},k_{c}} \right)}}}},} & (9)\end{matrix}$

where h is the pose distance function from Equation (7) and ƒ_(c) is afunction that maps the state vector to the quantity relevant for theconstraint c. For example, ƒ_(c) selects p_(i), from the state vectorfor a constraint on the absolute camera pose, or computes the visualodometry between camera poses for p_(i), and p_(j) for a constraint onthe relative camera pose. k_(c) is the observation for the constraint onthe relative camera pose, and remains constant throughout theoptimization process. For example, (1) For the absolute camera poseconstraints, k_(c) is the estimated camera pose produced by the camerapose estimation system 125 and (2) For the relative camera poseconstraints, k_(c) is the additional data input {circumflex over(v)}_(ij). The pose distance function h is defined as:

h (ƒ_(c)(c),k _(c))=(ƒ_(c)(z)−k _(c))^(T) S _(c)(ƒ_(c)(z)−k _(c))  (10)

where S_(c) is the covariance matrix for the constraint. In anembodiment, S_(c) is set to identity for all translation constraints andtuned to σI₃ (σ=10 to 35) for different environments.

FIG. 2B illustrates a flowchart of a method 220 for estimating camerapose using the camera pose estimation system shown in FIG. 2A, inaccordance with an embodiment. Steps 135, 155, 160, 165, and 170 arecompleted as previously described in conjunction with FIG. 1E. At step225, the estimated camera pose generated by the camera pose estimationsystem 100 or 125 is processed by the pose graph optimization unit 215using PGO to produce the refined camera pose. In an embodiment, the posegraph optimization unit 215 processes pairs of estimated camera poses toproduce pairs of refined camera poses.

FIG. 2C illustrates camera localization results, in accordance with anembodiment. Plot 240 includes a ground truth camera trajectory (path)242 that is 1120 meters long for an autonomous driving application. ThePlot 240 also includes a trajectory of estimated camera poses 244beginning at a first frame 241. The trajectory 244 is generated by aprior art camera localization system using stereo visual odometry andhas a mean translation error of 40.20 and a mean rotation error of 12.85degrees. Trajectory 244 drifts away from the ground truth cameratrajectory 242.

Plot 245 includes the trajectory 242 overlaid with a trajectory ofestimated camera poses 246 beginning at the first frame, where thetrajectory 246 is generated by the camera pose estimation system 100(trained using supervision) and has a mean translation error of 9.84 anda mean rotation error of 3.96 degrees. Plot 250 includes the trajectory242 overlaid with a trajectory of estimated camera poses 252 beginningat the first frame, where the trajectory 252 is generated by the camerapose estimation system 125 (trained using supervision andself-supervision) and has a mean translation error of 8.17 and a meanrotation error of 2.62 degrees. As shown by plot 255, the meantranslation error and a mean rotation error for the camera poseestimation system 125 may be reduced to by doubling the number of inputimage sequences for self-supervised training to 6.95 and 2.38 degrees,respectively. Plot 260 includes the trajectory 242 overlaid with atrajectory of estimated camera poses 262 beginning at the first frame,where the trajectory 262 is generated by the camera pose estimationsystem 125 (trained using supervision and self-supervision) using GPSdata and the additional data input to the training loss unit 115 and hasa mean translation error of 6.78 and a mean rotation error of 2.72degrees. Plot 265 includes the trajectory 242 overlaid with a trajectoryof estimated camera poses 266 beginning at the first frame, where thetrajectory 266 is generated by the camera pose estimation system 200(trained using supervision and self-supervision) and has a meantranslation error of 6.73 and a mean rotation error of 2.23 degrees.

While the camera pose estimation system 100 produces a more accuratetrajectory 246 compared with the trajectory 244, the use ofself-supervision for the camera pose estimation system 125 furtherimproves the accuracy. Finally, the use of PGO in the camera poseestimation system 200 produces the most accurate trajectory 266. Outlierestimated camera pose positions in the plots shown in FIG. 2C oftencorrespond to images with large over-exposed regions, and can befiltered out with simple post-processing (e.g. temporal medianfiltering).

In summary, camera pose estimation systems 100, 125, and 200 learn ageneral, data-driven map representation for camera localization. Theweights of the DNN 110 are learned to define the map representation ofan environment captured by a camera. The weights may be continuouslyupdated during inferencing with self-supervision using unlabeled data(input images, translation constraints from two GPS readings, poseconstraints from visual odometry between pairs of images, rotationconstraints from two IMU readings, etc). The DNN 110 is trained(supervised and self-supervised) using pairs of input images and theinput images in each pair are not necessarily adjacent in a sequence.Finally, a moving-window PGO may be employed during inferencing toobtain a smooth and drift free camera trajectory by fusing the estimatedcamera pose predictions and visual odometry data.

Parallel Processing Architecture

FIG. 3 illustrates a parallel processing unit (PPU) 300, in accordancewith an embodiment. In an embodiment, the PPU 300 is a multi-threadedprocessor that is implemented on one or more integrated circuit devices.The PPU 300 is a latency hiding architecture designed to process manythreads in parallel. A thread (i.e., a thread of execution) is aninstantiation of a set of instructions configured to be executed by thePPU 300. In an embodiment, the PPU 300 is a graphics processing unit(GPU) configured to implement a graphics rendering pipeline forprocessing three-dimensional (3D) graphics data in order to generatetwo-dimensional (2D) image data for display on a display device such asa liquid crystal display (LCD) device. In other embodiments, the PPU 300may be utilized for performing general-purpose computations. While oneexemplary parallel processor is provided herein for illustrativepurposes, it should be strongly noted that such processor is set forthfor illustrative purposes only, and that any processor may be employedto supplement and/or substitute for the same.

One or more PPUs 300 may be configured to accelerate thousands of HighPerformance Computing (HPC), data center, and machine learningapplications. The PPU 300 may be configured to accelerate numerous deeplearning systems and applications including autonomous vehicleplatforms, deep learning, high-accuracy speech, image, and textrecognition systems, intelligent video analytics, molecular simulations,drug discovery, disease diagnosis, weather forecasting, big dataanalytics, astronomy, molecular dynamics simulation, financial modeling,robotics, factory automation, real-time language translation, onlinesearch optimizations, and personalized user recommendations, and thelike.

As shown in FIG. 3, the PPU 300 includes an Input/Output (I/O) unit 305,a front end unit 315, a scheduler unit 320, a work distribution unit325, a hub 330, a crossbar (Xbar) 370, one or more general processingclusters (GPCs) 350, and one or more partition units 380. The PPU 300may be connected to a host processor or other PPUs 300 via one or morehigh-speed NVLink 310 interconnect. The PPU 300 may be connected to ahost processor or other peripheral devices via an interconnect 302. ThePPU 300 may also be connected to a local memory comprising a number ofmemory devices 304. In an embodiment, the local memory may comprise anumber of dynamic random access memory (DRAM) devices. The DRAM devicesmay be configured as a high-bandwidth memory (HBM) subsystem, withmultiple DRAM dies stacked within each device.

The NVLink 310 interconnect enables systems to scale and include one ormore PPUs 300 combined with one or more CPUs, supports cache coherencebetween the PPUs 300 and CPUs, and CPU mastering. Data and/or commandsmay be transmitted by the NVLink 310 through the hub 330 to/from otherunits of the PPU 300 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).The NVLink 310 is described in more detail in conjunction with FIG. 5B.

The I/O unit 305 is configured to transmit and receive communications(i.e., commands, data, etc.) from a host processor (not shown) over theinterconnect 302. The I/O unit 305 may communicate with the hostprocessor directly via the interconnect 302 or through one or moreintermediate devices such as a memory bridge. In an embodiment, the I/Ounit 305 may communicate with one or more other processors, such as oneor more the PPUs 300 via the interconnect 302. In an embodiment, the I/Ounit 305 implements a Peripheral Component Interconnect Express (PCIe)interface for communications over a PCIe bus and the interconnect 302 isa PCIe bus. In alternative embodiments, the I/O unit 305 may implementother types of well-known interfaces for communicating with externaldevices.

The I/O unit 305 decodes packets received via the interconnect 302. Inan embodiment, the packets represent commands configured to cause thePPU 300 to perform various operations. The I/O unit 305 transmits thedecoded commands to various other units of the PPU 300 as the commandsmay specify. For example, some commands may be transmitted to the frontend unit 315. Other commands may be transmitted to the hub 330 or otherunits of the PPU 300 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).In other words, the I/O unit 305 is configured to route communicationsbetween and among the various logical units of the PPU 300.

In an embodiment, a program executed by the host processor encodes acommand stream in a buffer that provides workloads to the PPU 300 forprocessing. A workload may comprise several instructions and data to beprocessed by those instructions. The buffer is a region in a memory thatis accessible (i.e., read/write) by both the host processor and the PPU300. For example, the I/O unit 305 may be configured to access thebuffer in a system memory connected to the interconnect 302 via memoryrequests transmitted over the interconnect 302. In an embodiment, thehost processor writes the command stream to the buffer and thentransmits a pointer to the start of the command stream to the PPU 300.The front end unit 315 receives pointers to one or more command streams.The front end unit 315 manages the one or more streams, reading commandsfrom the streams and forwarding commands to the various units of the PPU300.

The front end unit 315 is coupled to a scheduler unit 320 thatconfigures the various GPCs 350 to process tasks defined by the one ormore streams. The scheduler unit 320 is configured to track stateinformation related to the various tasks managed by the scheduler unit320. The state may indicate which GPC 350 a task is assigned to, whetherthe task is active or inactive, a priority level associated with thetask, and so forth. The scheduler unit 320 manages the execution of aplurality of tasks on the one or more GPCs 350.

The scheduler unit 320 is coupled to a work distribution unit 325 thatis configured to dispatch tasks for execution on the GPCs 350. The workdistribution unit 325 may track a number of scheduled tasks receivedfrom the scheduler unit 320. In an embodiment, the work distributionunit 325 manages a pending task pool and an active task pool for each ofthe GPCs 350. The pending task pool may comprise a number of slots(e.g., 32 slots) that contain tasks assigned to be processed by aparticular GPC 350. The active task pool may comprise a number of slots(e.g., 4 slots) for tasks that are actively being processed by the GPCs350. As a GPC 350 finishes the execution of a task, that task is evictedfrom the active task pool for the GPC 350 and one of the other tasksfrom the pending task pool is selected and scheduled for execution onthe GPC 350. If an active task has been idle on the GPC 350, such aswhile waiting for a data dependency to be resolved, then the active taskmay be evicted from the GPC 350 and returned to the pending task poolwhile another task in the pending task pool is selected and scheduledfor execution on the GPC 350.

The work distribution unit 325 communicates with the one or more GPCs350 via XBar 370. The XBar 370 is an interconnect network that couplesmany of the units of the PPU 300 to other units of the PPU 300. Forexample, the XBar 370 may be configured to couple the work distributionunit 325 to a particular GPC 350. Although not shown explicitly, one ormore other units of the PPU 300 may also be connected to the XBar 370via the hub 330.

The tasks are managed by the scheduler unit 320 and dispatched to a GPC350 by the work distribution unit 325. The GPC 350 is configured toprocess the task and generate results. The results may be consumed byother tasks within the GPC 350, routed to a different GPC 350 via theXBar 370, or stored in the memory 304. The results can be written to thememory 304 via the partition units 380, which implement a memoryinterface for reading and writing data to/from the memory 304. Theresults can be transmitted to another PPU 304 or CPU via the NVLink 310.In an embodiment, the PPU 300 includes a number U of partition units 380that is equal to the number of separate and distinct memory devices 304coupled to the PPU 300. A partition unit 380 will be described in moredetail below in conjunction with FIG. 4B.

In an embodiment, a host processor executes a driver kernel thatimplements an application programming interface (API) that enables oneor more applications executing on the host processor to scheduleoperations for execution on the PPU 300. In an embodiment, multiplecompute applications are simultaneously executed by the PPU 300 and thePPU 300 provides isolation, quality of service (QoS), and independentaddress spaces for the multiple compute applications. An application maygenerate instructions (i.e., API calls) that cause the driver kernel togenerate one or more tasks for execution by the PPU 300. The driverkernel outputs tasks to one or more streams being processed by the PPU300. Each task may comprise one or more groups of related threads,referred to herein as a warp. In an embodiment, a warp comprises 32related threads that may be executed in parallel. Cooperating threadsmay refer to a plurality of threads including instructions to performthe task and that may exchange data through shared memory. Threads andcooperating threads are described in more detail in conjunction withFIG. 5A.

FIG. 4A illustrates a GPC 350 of the PPU 300 of FIG. 3, in accordancewith an embodiment. As shown in FIG. 4A, each GPC 350 includes a numberof hardware units for processing tasks. In an embodiment, each GPC 350includes a pipeline manager 410, a pre-raster operations unit (PROP)415, a raster engine 425, a work distribution crossbar (WDX) 480, amemory management unit (MMU) 490, and one or more Data ProcessingClusters (DPCs) 420. It will be appreciated that the GPC 350 of FIG. 4Amay include other hardware units in lieu of or in addition to the unitsshown in FIG. 4A.

In an embodiment, the operation of the GPC 350 is controlled by thepipeline manager 410. The pipeline manager 410 manages the configurationof the one or more DPCs 420 for processing tasks allocated to the GPC350. In an embodiment, the pipeline manager 410 may configure at leastone of the one or more DPCs 420 to implement at least a portion of agraphics rendering pipeline. For example, a DPC 420 may be configured toexecute a vertex shader program on the programmable streamingmultiprocessor (SM) 440. The pipeline manager 410 may also be configuredto route packets received from the work distribution unit 325 to theappropriate logical units within the GPC 350. For example, some packetsmay be routed to fixed function hardware units in the PROP 415 and/orraster engine 425 while other packets may be routed to the DPCs 420 forprocessing by the primitive engine 435 or the SM 440. In an embodiment,the pipeline manager 410 may configure at least one of the one or moreDPCs 420 to implement a neural network model and/or a computingpipeline.

The PROP unit 415 is configured to route data generated by the rasterengine 425 and the DPCs 420 to a Raster Operations (ROP) unit, describedin more detail in conjunction with FIG. 4B. The PROP unit 415 may alsobe configured to perform optimizations for color blending, organizepixel data, perform address translations, and the like.

The raster engine 425 includes a number of fixed function hardware unitsconfigured to perform various raster operations. In an embodiment, theraster engine 425 includes a setup engine, a coarse raster engine, aculling engine, a clipping engine, a fine raster engine, and a tilecoalescing engine. The setup engine receives transformed vertices andgenerates plane equations associated with the geometric primitivedefined by the vertices. The plane equations are transmitted to thecoarse raster engine to generate coverage information (e.g., an x,ycoverage mask for a tile) for the primitive. The output of the coarseraster engine is transmitted to the culling engine where fragmentsassociated with the primitive that fail a z-test are culled, andtransmitted to a clipping engine where fragments lying outside a viewingfrustum are clipped. Those fragments that survive clipping and cullingmay be passed to the fine raster engine to generate attributes for thepixel fragments based on the plane equations generated by the setupengine. The output of the raster engine 425 comprises fragments to beprocessed, for example, by a fragment shader implemented within a DPC420.

Each DPC 420 included in the GPC 350 includes an M-Pipe Controller (MPC)430, a primitive engine 435, and one or more SMs 440. The MPC 430controls the operation of the DPC 420, routing packets received from thepipeline manager 410 to the appropriate units in the DPC 420. Forexample, packets associated with a vertex may be routed to the primitiveengine 435, which is configured to fetch vertex attributes associatedwith the vertex from the memory 304. In contrast, packets associatedwith a shader program may be transmitted to the SM 440.

The SM 440 comprises a programmable streaming processor that isconfigured to process tasks represented by a number of threads. Each SM440 is multi-threaded and configured to execute a plurality of threads(e.g., 32 threads) from a particular group of threads concurrently. Inan embodiment, the SM 440 implements a SIMD (Single-Instruction,Multiple-Data) architecture where each thread in a group of threads(i.e., a warp) is configured to process a different set of data based onthe same set of instructions. All threads in the group of threadsexecute the same instructions. In another embodiment, the SM 440implements a SIMT (Single-Instruction, Multiple Thread) architecturewhere each thread in a group of threads is configured to process adifferent set of data based on the same set of instructions, but whereindividual threads in the group of threads are allowed to diverge duringexecution. In an embodiment, a program counter, call stack, andexecution state is maintained for each warp, enabling concurrencybetween warps and serial execution within warps when threads within thewarp diverge. In another embodiment, a program counter, call stack, andexecution state is maintained for each individual thread, enabling equalconcurrency between all threads, within and between warps. Whenexecution state is maintained for each individual thread, threadsexecuting the same instructions may be converged and executed inparallel for maximum efficiency. The SM 440 will be described in moredetail below in conjunction with FIG. 5A.

The MMU 490 provides an interface between the GPC 350 and the partitionunit 380. The MMU 490 may provide translation of virtual addresses intophysical addresses, memory protection, and arbitration of memoryrequests. In an embodiment, the MMU 490 provides one or more translationlookaside buffers (TLBs) for performing translation of virtual addressesinto physical addresses in the memory 304.

FIG. 4B illustrates a memory partition unit 380 of the PPU 300 of FIG.3, in accordance with an embodiment. As shown in FIG. 4B, the memorypartition unit 380 includes a Raster Operations (ROP) unit 450, a leveltwo (L2) cache 460, and a memory interface 470. The memory interface 470is coupled to the memory 304. Memory interface 470 may implement 32, 64,128, 1024-bit data buses, or the like, for high-speed data transfer. Inan embodiment, the PPU 300 incorporates U memory interfaces 470, onememory interface 470 per pair of partition units 380, where each pair ofpartition units 380 is connected to a corresponding memory device 304.For example, PPU 300 may be connected to up to Y memory devices 304,such as high bandwidth memory stacks or graphics double-data-rate,version 5, synchronous dynamic random access memory, or other types ofpersistent storage.

In an embodiment, the memory interface 470 implements an HBM2 memoryinterface and Y equals half U. In an embodiment, the HBM2 memory stacksare located on the same physical package as the PPU 300, providingsubstantial power and area savings compared with conventional GDDR5SDRAM systems. In an embodiment, each HBM2 stack includes four memorydies and Y equals 4, with HBM2 stack including two 128-bit channels perdie for a total of 8 channels and a data bus width of 1024 bits.

In an embodiment, the memory 304 supports Single-Error CorrectingDouble-Error Detecting (SECDED) Error Correction Code (ECC) to protectdata. ECC provides higher reliability for compute applications that aresensitive to data corruption. Reliability is especially important inlarge-scale cluster computing environments where PPUs 300 process verylarge datasets and/or run applications for extended periods.

In an embodiment, the PPU 300 implements a multi-level memory hierarchy.In an embodiment, the memory partition unit 380 supports a unifiedmemory to provide a single unified virtual address space for CPU and PPU300 memory, enabling data sharing between virtual memory systems. In anembodiment the frequency of accesses by a PPU 300 to memory located onother processors is traced to ensure that memory pages are moved to thephysical memory of the PPU 300 that is accessing the pages morefrequently. In an embodiment, the NVLink 310 supports addresstranslation services allowing the PPU 300 to directly access a CPU'spage tables and providing full access to CPU memory by the PPU 300.

In an embodiment, copy engines transfer data between multiple PPUs 300or between PPUs 300 and CPUs. The copy engines can generate page faultsfor addresses that are not mapped into the page tables. The memorypartition unit 380 can then service the page faults, mapping theaddresses into the page table, after which the copy engine can performthe transfer. In a conventional system, memory is pinned (i.e.,non-pageable) for multiple copy engine operations between multipleprocessors, substantially reducing the available memory. With hardwarepage faulting, addresses can be passed to the copy engines withoutworrying if the memory pages are resident, and the copy process istransparent.

Data from the memory 304 or other system memory may be fetched by thememory partition unit 380 and stored in the L2 cache 460, which islocated on-chip and is shared between the various GPCs 350. As shown,each memory partition unit 380 includes a portion of the L2 cache 460associated with a corresponding memory device 304. Lower level cachesmay then be implemented in various units within the GPCs 350. Forexample, each of the SMs 440 may implement a level one (L1) cache. TheL1 cache is private memory that is dedicated to a particular SM 440.Data from the L2 cache 460 may be fetched and stored in each of the L1caches for processing in the functional units of the SMs 440. The L2cache 460 is coupled to the memory interface 470 and the XBar 370.

The ROP unit 450 performs graphics raster operations related to pixelcolor, such as color compression, pixel blending, and the like. The ROPunit 450 also implements depth testing in conjunction with the rasterengine 425, receiving a depth for a sample location associated with apixel fragment from the culling engine of the raster engine 425. Thedepth is tested against a corresponding depth in a depth buffer for asample location associated with the fragment. If the fragment passes thedepth test for the sample location, then the ROP unit 450 updates thedepth buffer and transmits a result of the depth test to the rasterengine 425. It will be appreciated that the number of partition units380 may be different than the number of GPCs 350 and, therefore, eachROP unit 450 may be coupled to each of the GPCs 350. The ROP unit 450tracks packets received from the different GPCs 350 and determines whichGPC 350 that a result generated by the ROP unit 450 is routed to throughthe Xbar 370. Although the ROP unit 450 is included within the memorypartition unit 380 in FIG. 4B, in other embodiment, the ROP unit 450 maybe outside of the memory partition unit 380. For example, the ROP unit450 may reside in the GPC 350 or another unit.

FIG. 5A illustrates the streaming multi-processor 440 of FIG. 4A, inaccordance with an embodiment. As shown in FIG. 5A, the SM 440 includesan instruction cache 505, one or more scheduler units 510, a registerfile 520, one or more processing cores 550, one or more special functionunits (SFUs) 552, one or more load/store units (LSUs) 554, aninterconnect network 580, a shared memory/L1 cache 570.

As described above, the work distribution unit 325 dispatches tasks forexecution on the GPCs 350 of the PPU 300. The tasks are allocated to aparticular DPC 420 within a GPC 350 and, if the task is associated witha shader program, the task may be allocated to an SM 440. The schedulerunit 510 receives the tasks from the work distribution unit 325 andmanages instruction scheduling for one or more thread blocks assigned tothe SM 440. The scheduler unit 510 schedules thread blocks for executionas warps of parallel threads, where each thread block is allocated atleast one warp. In an embodiment, each warp executes 32 threads. Thescheduler unit 510 may manage a plurality of different thread blocks,allocating the warps to the different thread blocks and then dispatchinginstructions from the plurality of different cooperative groups to thevarious functional units (i.e., cores 550, SFUs 552, and LSUs 554)during each clock cycle.

Cooperative Groups is a programming model for organizing groups ofcommunicating threads that allows developers to express the granularityat which threads are communicating, enabling the expression of richer,more efficient parallel decompositions. Cooperative launch APIs supportsynchronization amongst thread blocks for the execution of parallelalgorithms. Conventional programming models provide a single, simpleconstruct for synchronizing cooperating threads: a barrier across allthreads of a thread block (i.e., the syncthreads( ) function). However,programmers would often like to define groups of threads at smaller thanthread block granularities and synchronize within the defined groups toenable greater performance, design flexibility, and software reuse inthe form of collective group-wide function interfaces.

Cooperative Groups enables programmers to define groups of threadsexplicitly at sub-block (i.e., as small as a single thread) andmulti-block granularities, and to perform collective operations such assynchronization on the threads in a cooperative group. The programmingmodel supports clean composition across software boundaries, so thatlibraries and utility functions can synchronize safely within theirlocal context without having to make assumptions about convergence.Cooperative Groups primitives enable new patterns of cooperativeparallelism, including producer-consumer parallelism, opportunisticparallelism, and global synchronization across an entire grid of threadblocks.

A dispatch unit 515 is configured to transmit instructions to one ormore of the functional units. In the embodiment, the scheduler unit 510includes two dispatch units 515 that enable two different instructionsfrom the same warp to be dispatched during each clock cycle. Inalternative embodiments, each scheduler unit 510 may include a singledispatch unit 515 or additional dispatch units 515.

Each SM 440 includes a register file 520 that provides a set ofregisters for the functional units of the SM 440. In an embodiment, theregister file 520 is divided between each of the functional units suchthat each functional unit is allocated a dedicated portion of theregister file 520. In another embodiment, the register file 520 isdivided between the different warps being executed by the SM 440. Theregister file 520 provides temporary storage for operands connected tothe data paths of the functional units.

Each SM 440 comprises L processing cores 550. In an embodiment, the SM440 includes a large number (e.g., 128, etc.) of distinct processingcores 550. Each core 550 may include a fully-pipelined,single-precision, double-precision, and/or mixed precision processingunit that includes a floating point arithmetic logic unit and an integerarithmetic logic unit. In an embodiment, the floating point arithmeticlogic units implement the IEEE 754-2008 standard for floating pointarithmetic. In an embodiment, the cores 550 include 64 single-precision(32-bit) floating point cores, 64 integer cores, 32 double-precision(64-bit) floating point cores, and 8 tensor cores.

Tensor cores configured to perform matrix operations, and, in anembodiment, one or more tensor cores are included in the cores 550. Inparticular, the tensor cores are configured to perform deep learningmatrix arithmetic, such as convolution operations for neural networktraining and inferencing. In an embodiment, each tensor core operates ona 4×4 matrix and performs a matrix multiply and accumulate operationD=A×B+C, where A, B, C, and D are 4×4 matrices.

In an embodiment, the matrix multiply inputs A and B are 16-bit floatingpoint matrices, while the accumulation matrices C and D may be 16-bitfloating point or 32-bit floating point matrices. Tensor Cores operateon 16-bit floating point input data with 32-bit floating pointaccumulation. The 16-bit floating point multiply requires 64 operationsand results in a full precision product that is then accumulated using32-bit floating point addition with the other intermediate products fora 4×4×4 matrix multiply. In practice, Tensor Cores are used to performmuch larger two-dimensional or higher dimensional matrix operations,built up from these smaller elements. An API, such as CUDA 9 C++ API,exposes specialized matrix load, matrix multiply and accumulate, andmatrix store operations to efficiently use Tensor Cores from a CUDA-C++program. At the CUDA level, the warp-level interface assumes 16×16 sizematrices spanning all 32 threads of the warp.

Each SM 440 also comprises M SFUs 552 that perform special functions(e.g., attribute evaluation, reciprocal square root, and the like). Inan embodiment, the SFUs 552 may include a tree traversal unit configuredto traverse a hierarchical tree data structure. In an embodiment, theSFUs 552 may include texture unit configured to perform texture mapfiltering operations. In an embodiment, the texture units are configuredto load texture maps (e.g., a 2D array of texels) from the memory 304and sample the texture maps to produce sampled texture values for use inshader programs executed by the SM 440. In an embodiment, the texturemaps are stored in the shared memory/L1 cache 470. The texture unitsimplement the fixture operations such as filtering operations usingmip-maps (i.e., texture maps of varying levels of detail). In anembodiment, each SM 340 includes two texture units.

Each SM 440 also comprises N LSUs 554 that implement load and storeoperations between the shared memory/L1 cache 570 and the register file520. Each SM 440 includes an interconnect network 580 that connects eachof the functional units to the register file 520 and the LSU 554 to theregister file 520, shared memory/L1 cache 570. In an embodiment, theinterconnect network 580 is a crossbar that can be configured to connectany of the functional units to any of the registers in the register file520 and connect the LSUs 554 to the register file and memory locationsin shared memory/L1 cache 570.

The shared memory/L1 cache 570 is an array of on-chip memory that allowsfor data storage and communication between the SM 440 and the primitiveengine 435 and between threads in the SM 440. In an embodiment, theshared memory/L1 cache 570 comprises 128 KB of storage capacity and isin the path from the SM 440 to the partition unit 380. The sharedmemory/L1 cache 570 can be used to cache reads and writes. One or moreof the shared memory/L1 cache 570, L2 cache 460, and memory 304 arebacking stores.

Combining data cache and shared memory functionality into a singlememory block provides the best overall performance for both types ofmemory accesses. The capacity is usable as a cache by programs that donot use shared memory. For example, if shared memory is configured touse half of the capacity, texture and load/store operations can use theremaining capacity. Integration within the shared memory/L1 cache 570enables the shared memory/L1 cache 570 to function as a high-throughputconduit for streaming data while simultaneously providing high-bandwidthand low-latency access to frequently reused data.

When configured for general purpose parallel computation, a simplerconfiguration can be used compared with graphics processing.Specifically, the fixed function graphics processing units shown in FIG.3, are bypassed, creating a much simpler programming model. In thegeneral purpose parallel computation configuration, the workdistribution unit 325 assigns and distributes blocks of threads directlyto the DPCs 420. The threads in a block execute the same program, usinga unique thread ID in the calculation to ensure each thread generatesunique results, using the SM 440 to execute the program and performcalculations, shared memory/L1 cache 570 to communicate between threads,and the LSU 554 to read and write global memory through the sharedmemory/L1 cache 570 and the memory partition unit 380. When configuredfor general purpose parallel computation, the SM 440 can also writecommands that the scheduler unit 320 can use to launch new work on theDPCs 420.

The PPU 300 may be included in a desktop computer, a laptop computer, atablet computer, servers, supercomputers, a smart-phone (e.g., awireless, hand-held device), personal digital assistant (PDA), a digitalcamera, a vehicle, a head mounted display, a hand-held electronicdevice, and the like. In an embodiment, the PPU 300 is embodied on asingle semiconductor substrate. In another embodiment, the PPU 300 isincluded in a system-on-a-chip (SoC) along with one or more otherdevices such as additional PPUs 300, the memory 204, a reducedinstruction set computer (RISC) CPU, a memory management unit (MMU), adigital-to-analog converter (DAC), and the like.

In an embodiment, the PPU 300 may be included on a graphics card thatincludes one or more memory devices 304. The graphics card may beconfigured to interface with a PCIe slot on a motherboard of a desktopcomputer. In yet another embodiment, the PPU 300 may be an integratedgraphics processing unit (iGPU) or parallel processor included in thechipset of the motherboard.

Exemplary Computing System

Systems with multiple GPUs and CPUs are used in a variety of industriesas developers expose and leverage more parallelism in applications suchas artificial intelligence computing. High-performance GPU-acceleratedsystems with tens to many thousands of compute nodes are deployed indata centers, research facilities, and supercomputers to solve everlarger problems. As the number of processing devices within thehigh-performance systems increases, the communication and data transfermechanisms need to scale to support the increased bandwidth.

FIG. 5B is a conceptual diagram of a processing system 500 implementedusing the PPU 300 of FIG. 3, in accordance with an embodiment. Theexemplary system 565 may be configured to implement the method 130 shownin FIG. 1C, the method 150 shown in FIG. 1E, and/or the method 220 shownin FIG. 2B. The processing system 500 includes a CPU 530, switch 510,and multiple PPUs 300 each and respective memories 304. The NVLink 310provides high-speed communication links between each of the PPUs 300.Although a particular number of NVLink 310 and interconnect 302connections are illustrated in FIG. 5B, the number of connections toeach PPU 300 and the CPU 530 may vary. The switch 510 interfaces betweenthe interconnect 302 and the CPU 530. The PPUs 300, memories 304, andNVLinks 310 may be situated on a single semiconductor platform to form aparallel processing module 525. In an embodiment, the switch 510supports two or more protocols to interface between various differentconnections and/or links.

In another embodiment (not shown), the NVLink 310 provides one or morehigh-speed communication links between each of the PPUs 300 and the CPU530 and the switch 510 interfaces between the interconnect 302 and eachof the PPUs 300. The PPUs 300, memories 304, and interconnect 302 may besituated on a single semiconductor platform to form a parallelprocessing module 525. In yet another embodiment (not shown), theinterconnect 302 provides one or more communication links between eachof the PPUs 300 and the CPU 530 and the switch 510 interfaces betweeneach of the PPUs 300 using the NVLink 310 to provide one or morehigh-speed communication links between the PPUs 300. In anotherembodiment (not shown), the NVLink 310 provides one or more high-speedcommunication links between the PPUs 300 and the CPU 530 through theswitch 510. In yet another embodiment (not shown), the interconnect 302provides one or more communication links between each of the PPUs 300directly. One or more of the NVLink 310 high-speed communication linksmay be implemented as a physical NVLink interconnect or either anon-chip or on-die interconnect using the same protocol as the NVLink310.

In the context of the present description, a single semiconductorplatform may refer to a sole unitary semiconductor-based integratedcircuit fabricated on a die or chip. It should be noted that the termsingle semiconductor platform may also refer to multi-chip modules withincreased connectivity which simulate on-chip operation and makesubstantial improvements over utilizing a conventional busimplementation. Of course, the various circuits or devices may also besituated separately or in various combinations of semiconductorplatforms per the desires of the user. Alternately, the parallelprocessing module 525 may be implemented as a circuit board substrateand each of the PPUs 300 and/or memories 304 may be packaged devices. Inan embodiment, the CPU 530, switch 510, and the parallel processingmodule 525 are situated on a single semiconductor platform.

In an embodiment, the signaling rate of each NVLink 310 is 20 to 25Gigabits/second and each PPU 300 includes six NVLink 310 interfaces (asshown in FIG. 5B, five NVLink 310 interfaces are included for each PPU300). Each NVLink 310 provides a data transfer rate of 25Gigabytes/second in each direction, with six links providing 300Gigabytes/second. The NVLinks 310 can be used exclusively for PPU-to-PPUcommunication as shown in FIG. 5B, or some combination of PPU-to-PPU andPPU-to-CPU, when the CPU 530 also includes one or more NVLink 310interfaces.

In an embodiment, the NVLink 310 allows direct load/store/atomic accessfrom the CPU 530 to each PPU's 300 memory 304. In an embodiment, theNVLink 310 supports coherency operations, allowing data read from thememories 304 to be stored in the cache hierarchy of the CPU 530,reducing cache access latency for the CPU 530. In an embodiment, theNVLink 310 includes support for Address Translation Services (ATS),allowing the PPU 300 to directly access page tables within the CPU 530.One or more of the NVLinks 310 may also be configured to operate in alow-power mode.

FIG. 5C illustrates an exemplary system 565 in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented. The exemplary system 565 may be configured toimplement the method 130 shown in FIG. 1C, the method 150 shown in FIG.1E, and/or the method 220 shown in FIG. 2B.

As shown, a system 565 is provided including at least one centralprocessing unit 530 that is connected to a communication bus 575. Thecommunication bus 575 may be implemented using any suitable protocol,such as PCI (Peripheral Component Interconnect), PCI-Express, AGP(Accelerated Graphics Port), HyperTransport, or any other bus orpoint-to-point communication protocol(s). The system 565 also includes amain memory 540. Control logic (software) and data are stored in themain memory 540 which may take the form of random access memory (RAM).

The system 565 also includes input devices 560, the parallel processingsystem 525, and display devices 545, i.e. a conventional CRT (cathoderay tube), LCD (liquid crystal display), LED (light emitting diode),plasma display or the like. User input may be received from the inputdevices 560, e.g., keyboard, mouse, touchpad, microphone, and the like.Each of the foregoing modules and/or devices may even be situated on asingle semiconductor platform to form the system 565. Alternately, thevarious modules may also be situated separately or in variouscombinations of semiconductor platforms per the desires of the user.

Further, the system 565 may be coupled to a network (e.g., atelecommunications network, local area network (LAN), wireless network,wide area network (WAN) such as the Internet, peer-to-peer network,cable network, or the like) through a network interface 535 forcommunication purposes.

The system 565 may also include a secondary storage (not shown). Thesecondary storage 610 includes, for example, a hard disk drive and/or aremovable storage drive, representing a floppy disk drive, a magnetictape drive, a compact disk drive, digital versatile disk (DVD) drive,recording device, universal serial bus (USB) flash memory. The removablestorage drive reads from and/or writes to a removable storage unit in awell-known manner.

Computer programs, or computer control logic algorithms, may be storedin the main memory 540 and/or the secondary storage. Such computerprograms, when executed, enable the system 565 to perform variousfunctions. The memory 540, the storage, and/or any other storage arepossible examples of computer-readable media.

The architecture and/or functionality of the various previous figuresmay be implemented in the context of a general computer system, acircuit board system, a game console system dedicated for entertainmentpurposes, an application-specific system, and/or any other desiredsystem. For example, the system 565 may take the form of a desktopcomputer, a laptop computer, a tablet computer, servers, supercomputers,a smart-phone (e.g., a wireless, hand-held device), personal digitalassistant (PDA), a digital camera, a vehicle, a head mounted display, ahand-held electronic device, a mobile phone device, a television,workstation, game consoles, embedded system, and/or any other type oflogic.

While various embodiments have been described above, it should beunderstood that they have been presented by way of example only, and notlimitation. Thus, the breadth and scope of a preferred embodiment shouldnot be limited by any of the above-described exemplary embodiments, butshould be defined only in accordance with the following claims and theirequivalents.

Machine Learning

Deep neural networks (DNNs) developed on processors, such as the PPU 300have been used for diverse use cases, from self-driving cars to fasterdrug development, from automatic image captioning in online imagedatabases to smart real-time language translation in video chatapplications. Deep learning is a technique that models the neurallearning process of the human brain, continually learning, continuallygetting smarter, and delivering more accurate results more quickly overtime. A child is initially taught by an adult to correctly identify andclassify various shapes, eventually being able to identify shapeswithout any coaching. Similarly, a deep learning or neural learningsystem needs to be trained in object recognition and classification forit get smarter and more efficient at identifying basic objects, occludedobjects, etc., while also assigning context to objects.

At the simplest level, neurons in the human brain look at various inputsthat are received, importance levels are assigned to each of theseinputs, and output is passed on to other neurons to act upon. Anartificial neuron or perceptron is the most basic model of a neuralnetwork. In one example, a perceptron may receive one or more inputsthat represent various features of an object that the perceptron isbeing trained to recognize and classify, and each of these features isassigned a certain weight based on the importance of that feature indefining the shape of an object.

A deep neural network (DNN) model includes multiple layers of manyconnected perceptrons (e.g., nodes) that can be trained with enormousamounts of input data to quickly solve complex problems with highaccuracy. In one example, a first layer of the DLL model breaks down aninput image of an automobile into various sections and looks for basicpatterns such as lines and angles. The second layer assembles the linesto look for higher level patterns such as wheels, windshields, andmirrors. The next layer identifies the type of vehicle, and the finalfew layers generate a label for the input image, identifying the modelof a specific automobile brand.

Once the DNN is trained, the DNN can be deployed and used to identifyand classify objects or patterns in a process known as inference.Examples of inference (the process through which a DNN extracts usefulinformation from a given input) include identifying handwritten numberson checks deposited into ATM machines, identifying images of friends inphotos, delivering movie recommendations to over fifty million users,identifying and classifying different types of automobiles, pedestrians,and road hazards in driverless cars, or translating human speech inreal-time.

During training, data flows through the DNN in a forward propagationphase until a prediction is produced that indicates a labelcorresponding to the input. If the neural network does not correctlylabel the input, then errors between the correct label and the predictedlabel are analyzed, and the weights are adjusted for each feature duringa backward propagation phase until the DNN correctly labels the inputand other inputs in a training dataset. Training complex neural networksrequires massive amounts of parallel computing performance, includingfloating-point multiplications and additions that are supported by thePPU 300. Inferencing is less compute-intensive than training, being alatency-sensitive process where a trained neural network is applied tonew inputs it has not seen before to classify images, translate speech,and generally infer new information.

Neural networks rely heavily on matrix math operations, and complexmulti-layered networks require tremendous amounts of floating-pointperformance and bandwidth for both efficiency and speed. With thousandsof processing cores, optimized for matrix math operations, anddelivering tens to hundreds of TFLOPS of performance, the PPU 300 is acomputing platform capable of delivering performance required for deepneural network-based artificial intelligence and machine learningapplications.

What is claimed is:
 1. A computer-implemented method, comprising:receiving an input image at a deep neural network (DNN), wherein weightsof the DNN define a map representation of an environment and the weightsare determined during training using a labeled training datasetincluding images and corresponding absolute camera poses and relativecamera poses; and applying, by the DNN, the weights to the input imageto generate an estimated camera pose for capturing the environment toproduce the input image.
 2. The computer-implemented method of claim 1,wherein, during the training, image pairs are input to the DNN andcorresponding estimated camera pose pairs are generated by the DNN. 3.The computer-implemented method of claim 2, wherein an image pairincludes a first image and an additional image in an image sequence, andone or more intervening images may occur between the first image and theadditional image.
 4. The computer-implemented method of claim 2,wherein, during the training, relative estimated camera poses arecomputed for each estimated camera pose pair.
 5. Thecomputer-implemented method of claim 4, wherein, during the training,the weights are modified to simultaneously reduce differences betweenthe relative estimated camera poses and the relative camera posesincluded in the training dataset and differences between the estimatedcamera poses generated by the DNN and the absolute camera poses includedin the training dataset.
 6. The computer-implemented method of claim 1,wherein a rotation portion of the estimated camera pose is parameterizedas a three-dimensional logarithm of a unit quaternion.
 7. Thecomputer-implemented method of claim 1, further comprising: receivingvisual odometry data corresponding to the input image; and modifying theweights of the DNN to minimize differences between the visual odometrydata and a relative camera pose computed using the estimated camera poseand an additional estimated camera pose generated by the DNN.
 8. Thecomputer-implemented method of claim 1, further comprising: receivingglobal position sensor data corresponding to the input image; andmodifying the weights to minimize differences between the globalposition sensor data and the estimated camera pose.
 9. Thecomputer-implemented method of claim 1, further comprising: receivinginertial measurement data corresponding to the input image; andmodifying the weights to minimize differences between the inertialmeasurement data and the estimated camera pose.
 10. Thecomputer-implemented method of claim 1, further comprisingpost-processing the estimated camera pose using pose graph optimization(PGO) to produce a refined camera pose.
 11. The computer-implementedmethod of claim 1, wherein the DNN comprises at least a convolutionalneural network layer, followed by a global average pooling layer,followed by a fully-connected layer to output the estimated camera pose.12. A system, comprising: a deep neural network (DNN) configured to:receive an input image, wherein weights of the DNN define a maprepresentation of an environment and the weights are determined duringtraining using a labeled training dataset including images andcorresponding absolute camera poses and relative camera poses; and applythe weights to the input image to generate an estimated camera pose forcapturing the environment to produce the input image.
 13. The system ofclaim 12, wherein, during the training, image pairs are input to the DNNand corresponding estimated camera pose pairs are generated by the DNN.14. The system of claim 13, wherein an image pair includes a first imageand an additional image in an image sequence, and one or moreintervening images may occur between the first image and the additionalimage.
 15. The system of claim 13, further comprising a relative posecomputation unit configured to compute relative estimated camera posesfor each estimated camera pose pair during the training.
 16. The systemof claim 15, further comprising a training loss unit configured tomodify the weights during the training to simultaneously reducedifferences between the relative estimated camera poses and the relativecamera poses included in the training dataset and differences betweenthe estimated camera poses generated by the DNN and the absolute cameraposes included in the training dataset.
 17. The system of claim 12,wherein a rotation portion of the estimated camera pose is parameterizedas a three-dimensional logarithm of a unit quaternion.
 18. The system ofclaim 12, further comprising a training loss unit configured to: receivevisual odometry data corresponding to the input image; and modify theweights of the DNN to minimize differences between the visual odometrydata and a relative camera pose computed using the estimated camera poseand an additional estimated camera pose generated by the DNN.
 19. Thesystem of claim 12, further comprising a pose graph optimization unitconfigured to post-process the estimated camera pose using pose graphoptimization (PGO) to produce a refined camera pose.
 20. Anon-transitory computer-readable media storing computer instructions forestimating camera poses that, when executed by one or more processors,cause the one or more processors to perform the steps of: receiving aninput image at a deep neural network (DNN), wherein weights of the DNNdefine a map representation of an environment and the weights aredetermined during training using a labeled training dataset includingimages and corresponding absolute camera poses and relative cameraposes; and applying, by the DNN, the weights to the input image togenerate an estimated camera pose for capturing the environment toproduce the input image.